AI Engineer, AVP

Barclays•Henderson, NV

About The Position

The AI Engineer uses data analytics and machine learning to extract insights from the bank’s data, inform strategic decisions, improve operational efficiency, and drive innovation. The role includes collecting and preparing data, maintaining automated data pipelines, developing statistical and machine learning models, and collaborating with business stakeholders to identify opportunities to create value through data science. At the Assistant Vice President level, the role contributes to policy development and operational effectiveness, collaborates across business functions, and applies professional expertise to work that affects the wider function. Depending on whether the position has leadership responsibilities or is an individual-contributor role, expectations include coaching employees or guiding collaborative assignments and team members. The position also involves complex analysis, stakeholder communication and influence, risk management, and strengthening controls. Barclays Services LLC is hiring for multiple positions in Henderson, Nevada. The role builds and deploys AI and generative AI applications, including retrieval-augmented generation, conversational AI, agentic workflows, and intelligent automation. It develops scalable backend services and integrations, operates cloud-native AI workloads on containerized platforms, and builds deployment, monitoring, and infrastructure automation capabilities. The engineer also designs secure networking, optimizes application performance and cost, implements MLOps/LLMOps practices, and partners with stakeholders to deliver production-ready AI in accordance with governance, security, and audit requirements.

Responsibilities

Identify, collect, and extract data from internal and external sources.
Clean, wrangle, and transform data to ensure its quality and suitability for analysis.
Develop and maintain efficient data pipelines for automated data acquisition and processing.
Design and conduct statistical and machine learning models to analyze data patterns, trends, and relationships.
Develop and implement predictive models to forecast outcomes and identify risks and opportunities.
Collaborate with business stakeholders to identify opportunities to create value through data science.
Build and deploy AI and generative AI applications, including RAG, conversational AI, agentic workflows, and intelligent automation.
Design and develop scalable backend services, APIs, and integrations connecting AI models with enterprise data platforms and business applications.
Architect and manage cloud-native AI workloads on containerized platforms, including Kubernetes and Amazon EKS.
Build and maintain CI/CD pipelines, infrastructure-as-code, model deployment frameworks, monitoring, and operational telemetry.
Design secure networking solutions, including VPC architecture, private connectivity, load balancing, service-to-service communication, API gateways, DNS, and network security controls.
Optimize AI application performance, scalability, availability, and cost efficiency across computing, storage, and networking layers.
Implement MLOps/LLMOps practices, including model versioning, automated testing, deployment, observability, rollback, and lifecycle management.
Partner with business and technology stakeholders to deploy production-ready AI capabilities while meeting governance, security, and audit requirements.
Contribute to policy development and operational effectiveness, collaborate across functions, and take ownership of risk management and control strengthening.
Communicate complex information and influence stakeholders to achieve outcomes.